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An error model for protein quantification

BIOINFORMATICS 2007年第20期23卷 2747-2753页

作者： Kreutz, C. Rodriguez, M. M. Bartolome Maiwald, T. Seidl, M. Blum, H. E. Mohr, L. Timmer, J. Freiburg Ctr Data Anal & Modeling FDM D-79014 Freiburg Germany Univ Freiburg Dept Phys D-79014 Freiburg Germany Univ Hosp Freiburg Dept Med 2 D-79106 Freiburg Germany

Motivation: Quantitative experimental data is the critical bottleneck in the modeling of dynamic cellular processes in systems biology. Here, we present statistical approaches improving reproducibility of protein quantification by immunoprecipitation and immunoblotting. Results: Based on a large data set with more than 3600 data points, we unravel that the main sources of biological variability and experimental noise are multiplicative and log-normally distributed. Therefore, we suggest a log-transformation of the data to obtain additive normally distributed noise. After this transformation, common statistical procedures can be applied to analyze the data. An error model is introduced to account for technical as well as biological variability. Elimination of these systematic errors decrease variability of measurements and allow for a more precise estimation of underlying dynamics of protein concentrations in cellular signaling. The proposed error model is relevant for simulation studies, parameter estimation and model selection, basic tools of systems biology.

关键词： DIFFERENTIALLY-EXPRESSED GENES microarray data QUANTITATIVE data IDENTIFICATION NORMALIZATION VARIANCE

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Transcriptomic signatures in breast cancer

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MOLECULAR BIOSYSTEMS 2007年第7期3卷 466-472页

作者： Fu, Jianjiang Jeffrey, Stefanie S. Stanford Univ Med Ctr Stanford CA 94305 USA

High throughput DNA microarray technology has been broadly applied to the study of breast cancer to classify molecular subtypes, to predict outcome, survival, response to treatment, and for the identification of novel therapeutic targets. Although results are promising, this technology will not have a full impact on routine clinical practice until there is further standardization of techniques and optimal clinical trial design. Due to substantial disease heterogeneity and the number of genes being analyzed, collaborative, multi-institutional studies are required to accrue enough patients for sufficient statistical power. Newer bioinformatic approaches are being developed to assist with the analysis of this important data.

关键词： GENE-EXPRESSION SIGNATURE NOTTINGHAM PROGNOSTIC INDEX microarray data MOLECULAR CLASSIFICATION ONCOGENIC PATHWAYS HISTOLOGIC GRADE ADJUVANT THERAPY METASTASIS SURVIVAL TUMORS

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Simultaneous testing of multiple hypotheses using generalized p-values

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STATISTICS & PROBABILITY LETTERS 2007年第12期77卷 1362-1370页

作者： Tsui, Kam-Wah Tang, Shijie Univ Wisconsin Dept Stat Madison WI 53706 USA

In the context of simultaneously testing many hypotheses, Benjainini and Hochberg [1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Statist. Soc. Set. B (Methodological) 57, 289-300] propose a procedure that guarantees that the false discovery rate (FDR) will be less than or equal to a specified value. Here, the FDR is the expected value of the ratio of the number of incorrectly rejected hypotheses and the total number of rejected hypotheses. To our knowledge, all of the existing research assumes that it usual p-value is available for each hypothesis. However, in circumstances when nuisance parameters are present, the usual p-values may not be free of the nuisance parameters. We develop a simultaneous testing procedure to control the FDR for the problem of simultaneously testing many Behrens-Fisher problems. Our multiple testing procedure, based on generalized p-values [Tsui, K.-W., Weerahandi, S., 1989. Generalized p-values in significance testing of hypotheses in the presence of nuisance parameters. J. Amer. Statist. Assoc. 84(406), 602-607], is then illustrated with in application to data from a microarray experiment. (c) 2007 Elsevier B.V. All rights reserved.

关键词： Behrens-Fisher problem false discovery rate ratio of two normal means microarray data

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A comparison of bootstrap methods and an adjusted bootstrap approach for estimating the prediction error in microarray classification

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STATISTICS IN MEDICINE 2007年第29期26卷 5320-5334页

作者： Jiang, Wenyu Simon, Richard Concordia Univ Dept Math & Stat Montreal PQ H3G 1M8 Canada NCI NIH Div Canc Treatment & Diag Biometr Res Branch Rockville MD 20852 USA

This paper first provides a critical review on some existing methods for estimating the prediction error in classifying microarray data where the number of genes greatly exceeds the number of specimens. Special attention is given to the bootstrap-related methods. When the sample size n is small, we find that all the reviewed methods suffer from either substantial bias or variability. We introduce a repeated leave-one-out bootstrap (RLOOB) method that predicts for each specimen in the sample using bootstrap learning sets of size ln. We then propose an adjusted bootstrap (ABS) method that fits a learning curve to the RLOOB estimates calculated with different bootstrap learning set sizes. The ABS method is robust across the situations we investigate and provides a slightly conservative estimate for the prediction error. Even with small samples, it does not suffer from large upward bias as the leave-one-out bootstrap and the 0.632+bootstrap, and it does not suffer from large variability as the leave-one-out cross-validation in microarray applications. Copyright (c) 2007 John Wiley & Sons, Ltd.

关键词： bootstrap prediction error class prediction microarray data learning curve feature selection

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Mosclust: a software library for discovering significant structures in bio-molecular data

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BIOINFORMATICS 2007年第3期23卷 387-389页

作者： Valentini, Giorgio Univ Milan Dipartimento Sci Informaz Milan Italy

The R package mosclust (model order selection for clustering problems) implements algorithms based on the concept of stability for discovering significant structures in bio-molecular data. The software library provides stability indices obtained through different data perturbations methods (resampling, random projections, noise injection), as well as statistical tests to assess the significance of multi-level structures singled out from the data. Availability:http://***/similar to valenti/SW/mosclust/download/ mosclust_ *** Contact:valentini@*** Supplementary information:http://***/similar to valenti/SW/ mosclust.

关键词： microarray data TOOL

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Discovering tightly regulated and differentially expressed gene sets in whole genome expression data

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BIOINFORMATICS 2007年第2期23卷 E84-E90页

作者： Ye, Chun Eskin, Eleazar Univ Calif San Diego Bioinformat Program La Jolla CA 92093 USA Univ Calif San Diego Dept Comp Sci & Engn La Jolla CA 92093 USA

Motivation: Recently, a new type of expression data is being collected which aims to measure the effect of genetic variation on gene expression in pathways. In these datasets, expression profiles are constructed for multiple strains of the same model organism under the same condition. The goal of analyses of these data is to find differences in regulatory patterns due to genetic variation between strains, often without a phenotype of interest in mind. We present a new method based on notions of tight regulation and differential expression to look for sets of genes which appear to be significantly affected by genetic variation. Results: When we use categorical phenotype information, as in the Alzheimer's and diabetes datasets, our method finds many of the same gene sets as gene set enrichment analysis. In addition, our notion of correlated gene sets allows us to focus our efforts on biological processes subjected to tight regulation. In murine hematopoietic stem cells, we are able to discover significant gene sets independent of a phenotype of interest. Some of these gene sets are associated with several blood-related phenotypes.

关键词： microarray data STEM-CELLS PATHWAYS MICE ONTOLOGY GROWTH

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Summary of contributions to GAW15 group 16: Processing/normalization of expression traits

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GENETIC EPIDEMIOLOGY 2007年第Sup1期31卷 S132-S138页

作者： Labbe, Aurlie McClintick, Jeanette MartineZ, Maria Univ Laval Dept Math & Stat Quebec City PQ Canada Univ Laval Robert Giffard Ctr Rech Quebec City PQ Canada Indiana Univ Sch Med Dept Biochem & Mol Biol Indianapolis IN USA Univ Toulouse 3 Toulouse France INSERM U563 Ctr Physiopathol Toulouse Purpan Toulouse France

Here, we summarize the contributions to group 16 of Genetic Analysis Workshop 15, held in Florida, U.S.A. The theme of this group was preprocessing of expression quantitative trait loci (eQTL) studies using the Affymetrix platform. The objective of the Genetic Analysis Workshop 15 problem 1 dataset was to use transcript levels that are measured using DNA microarrays as quantitative traits and localize the genes or other features of the DNA that control gene expression by quantitative trait loci linkage analyses. All contributors of this group used the microarray expression profiles (problem 1) data. Various approaches and questions were examined to investigate the effects of preprocessing methods and/or gene filtering on the interpretation of data, specifically on heritability estimates of gene expression and on linkage results. In addition, some contributors focused on the statistical issues involved in large-scale genetic analyses of quantitative traits that account for or build composite phenotypes from a large number of correlated traits. Since the true eQTLs are not known in the problem I data, results from the 11 studies cannot be fully evaluated for the methods employed. However, several common trends were found. All reports concluded that preprocessing statistical analyses may have an important impact on eQTL analyses and on the identification of cis-/trans-regulators and/or major biological pathways.

关键词： gene expression eQTL analysis LOD score microarray data data processing

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Normality of gene expression revisited

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JOURNAL OF BIOLOGICAL SYSTEMS 2007年第1期15卷 39-48页

作者： Chen, Linlin Klebanov, Lev Yakovlev, Andrei Univ Rochester Dept Biostat & Computat Biol Rochester NY 14642 USA Acad Sci Czech Republ Inst Informat & Control Theory CZ-18675 Prague 8 Czech Republic Charles Univ Prague Dept Probabil & Stat CZ-18675 Prague 8 Czech Republic

A question has been raised in several publications as to whether or not the expression levels or their logarithms for different genes are normally distributed. To answer this question would require a large data set where both biological variability and technological noise are present. An earlier attempt to test this assumption was limited to technical replicates and did not take multiplicity of tests into account when assessing the net results of goodness-of-fit testing. Therefore, the problem calls for further exploration. We applied several statistical tests to a large set of high-density oligonucleotide microarray data in order to systematically test for log-normality of expression levels for all the reporter genes. The multiple testing aspect of the problem was addressed by designing a pertinent resampling procedure. The results of testing did not reject normality of log-intensities in the non-normalized data under study. However, the global log-normality hypothesis was rejected beyond all reasonable doubt when the data were normalized by the quantile normalization procedure. Our results are consistent with the hypothesis that non-normalized expression levels of different genes are approximately log-normally distributed. The quantile normalization causes dramatic changes in the shape of marginal distributions of log-intensities which may be an indication that this procedure interferes not only in the technological noise but the true biological signal as well. This possibility invites a special investigation.

关键词： gene expression microarray data goodness-of-fit normality resampling

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Pathway-based approaches for analysis of genomewide association studies

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AMERICAN JOURNAL OF HUMAN GENETICS 2007年第6期81卷 1278-1283页

作者： Wang, Kai Li, Mingyao Bucan, Maja Univ Penn Dept Genet Philadelphia PA 19104 USA Univ Penn Dept Biostat & Epidemiol Philadelphia PA 19104 USA

Published genomewide association (GWA) studies typically analyze and report single-nucleotide polymorphisms (SNPs) and their neighboring genes with the strongest evidence of association (the "most-significant SNPs/genes" approach), while paying little attention to the rest. Borrowing ideas from microarray data analysis, we demonstrate that pathway-based approaches, which jointly consider multiple contributing factors in the same pathway, might complement the most-significant SNPs/genes approach and provide additional insights into interpretation of GWA data on complex diseases.

关键词： .genetic variants genomewide microarray data modest Adjustment complex diseases multiple testing GWA studies strongest evidence SNPs/genes same pathway GWA data

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Systems biology via redescription and ontologies (I): Finding phase changes with applications to malaria temporal data

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Systems and Synthetic Biology 2007年第4期1卷 197-205页

作者： Kleinberg, Samantha Casey, Kevin Mishra, Bud Courant Institute of Mathematical Sciences New York University New York NY 10012 715 Broadway United States New York University School of Medicine New York NY United States

Biological systems are complex and often composed of many subtly interacting components. Furthermore, such systems evolve through time and, as the underlying biology executes its genetic program, the relationships between components change and undergo dynamic reorganization. Characterizing these relationships precisely is a challenging task, but one that must be undertaken if we are to understand these systems in sufficient detail. One set of tools that may prove useful are the formal principles of model building and checking, which could allow the biologist to frame these inherently temporal questions in a sufficiently rigorous framework. In response to these challenges, GOALIE (Gene ontology algorithmic logic and information extractor) was developed and has been successfully employed in the analysis of high throughput biological data (e.g. time-course gene-expression microarray data and neural spike train recordings). The method has applications to a wide variety of temporal data, indeed any data for which there exist ontological descriptions. This paper describes the algorithms behind GOALIE and its use in the study of the Intraerythrocytic Developmental Cycle (IDC) of Plasmodium falciparum, the parasite responsible for a deadly form of chloroquine resistant malaria. We focus in particular on the problem of finding phase changes, times of reorganization of transcriptional control. © 2008 The Author(s).

关键词： Information theory microarray data Model checking Ontology Redescription Timecourse data

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